Recognizing paralinguistic cues from speech has applications in varied domains of speech processing. In this paper we present approaches to identify the expressed intent from acoustics in the context of INTERSPEECH 2018 ComParE challenge. We have made submissions in three sub-challenges: prediction of 1) self-assessed affect and 2) atypical affect 3) Crying Sub challenge. Since emotion and intent are perceived at suprasegmental levels, we explore a variety of utterance level embeddings. The work includes experiments with both automatically derived as well as knowledge-inspired features that capture spoken intent at various acoustic levels. Incorporation of utterance level embeddings at the text level using an off the shelf phone decoder has also been investigated. The experiments impose constraints and manipulate the training procedure using heuristics from the data distribution. We conclude by presenting the preliminary results on the development and blind test sets.

In this paper, we describe a dataset and baseline result for a question answering that utilizes web tables. It contains commonly asked questions on the web and their corresponding answers found in tables on websites. Our dataset is novel in that every question is paired with a table of a different signature. In particular, the dataset contains two classes of tables: entity-instance tables and the key-value tables. Each QA instance comprises a table of either kind, a natural language question, and a corresponding structured SQL query. We build our model by dividing question answering into several tasks, including table retrieval and question element classification, and conduct experiments to measure the performance of each task. We extract various features specific to each task and compose a full pipeline which constructs the SQL query from its parts. Our work provides qualitative results and error analysis for each task, and identifies in detail the reasoning required to generate SQL expressions from natural language questions. This analysis of reasoning informs future models based on neural machine learning.